Jul 2026· Health Psychology and Behavioral Medicine· Vol 14· 0 citations· 32 references
Medicine
TL;DR
The Test Anxiety Predictors Inventory (TAPI) is developed and validated to support concurrent identification of at-risk students and to inform domain-specific, targeted interventions in resource-constrained medical training settings.
Abstract
Abstract Background Test anxiety (TA) is common among medical students and may adversely affect mental well-being and academic performance. However, existing instruments such as the Test Anxiety Inventory (TAI) predominantly quantify the severity and manifestations of anxiety (outcomes) rather than identifying modifiable, intervention-relevant antecedents (predictors). This study aimed to develop and validate the Test Anxiety Predictors Inventory (TAPI) to support concurrent identification of at-risk students and to inform domain-specific, targeted interventions in resource-constrained medical training settings. Methods A cross-sectional census survey was conducted at Can Tho University of Medicine and Pharmacy (Dec 2024–Mar 2025; 382/389 valid responses, 98.2%). A 30-item pool was piloted in 71 students, then refined through iterative EFA (3 rounds; ML extraction, Direct Oblimin) and CFA (ML and DWLS estimators). Internal consistency, convergent and discriminant validity, correlation with TAI, and concurrent classification performance (multivariable logistic regression; ROC-AUC) were assessed. Because census sampling yielded one dataset, EFA/CFA reflect internal testing pending external validation; ‘prediction’ denotes concurrent classification, not temporal forecasting. Results TA prevalence (TAI ≥ 48) was 67.0% (95% CI 62.15–71.54). The final 14-item TAPI loaded on three factors—EAS (7), AMF (4), SRP (3)—explaining 77.99% of variance (KMO = 0.935). The correlated three-factor CFA showed good fit (CFI = 0.957; RMSEA = 0.083). The second-order DWLS model showed excellent fit but produced a Heywood warning with negative latent variance for EAS; the correlated three-factor model is therefore the more defensible representation. Reliability was high (α/ω = 0.89–0.95; CR = 0.92–0.96; AVE = 0.74–0.82; HTMT < 0.85). TAPI–TAI correlation was r = 0.576; classification model AUC = 0.804, sensitivity = 0.938, specificity = 0.452. Conclusions TAPI shows promising internal structure and concurrent classification performance. High sensitivity supports first-stage screening, while modest specificity indicates it should complement, not replace, formal diagnostic procedures.
Introduction: The Big Five model is the most widely accepted theoretical framework for assessing personality traits, with applications in medical education, such as to predict academic performance, burnout, and student well-being. The BFI-10 is an ultra-short 10-item version designed for time-constrained contexts, although it lacks validation in Latin American medical students.
Objective: The objective of this study was to evaluate the evidence of validity of the BFI-10 in Peruvian medical students.
Method: A cross-sectional psychometric validation study was conducted during the 2024 academic year. The sample consisted of first- to seventh-year medical students at the Private University of Tacna (Peru). The factor structure was evaluated using confirmatory factor analysis, measurement invariance by sex and academic phase, internal consistency, and criterion validity against academic performance (continuous GPA) and history of mental-health care.
Results: A total of 248 medical students participated (54.4% women; mean age 22.9 years). The five-factor model showed adequate fit (CFI = 0.968; RMSEA = 0.064) with standardized loadings between 0.71 and 0.89. Measurement invariance held by academic phase and, with caution, by sex (configural RMSEA = 0.098). Internal consistency was acceptable (ω = 0.68–0.86), with Openness the lowest (ω = 0.68). No Big Five dimension correlated significantly with continuous GPA; however, Neuroticism was significantly higher among students with a history of mental-health care (p = 0.004, d = 0.55).
Conclusion: The BFI-10 shows adequate evidence of structural validity and measurement invariance (with caution for sex) in Peruvian medical students. Criterion-validity evidence remains preliminary; the instrument is suitable for group-level research rather than individual, selection, or screening decisions.
J. Flores-Cohaila, Brayan Miranda-Chávez, J. Huarcaya-Victoria et al.· Interacciones· 0 citations
Mental health issues, especially depressive symptoms, among young adults represent a public health challenge. Conventional psychological assessment tools have limited sensitivity and specificity for identifying individuals at risk. This study aims to develop an explainable machine learning-based model to stratify concurrent depression risk in young adults. This study included 100,257 college students and collected mental health variables including depression, anxiety, resilience, parent-child relationship, and duration of mobile phone usage. The screening capabilities of 13 machine learning algorithms were systematically evaluated and compared. The SHapley Additive exPlanations (SHAP) framework was employed for the interpretability of the final model. The median scores for parent-child relationship, resilience, anxiety, and mobile phone usage time was 42.0, 28.0, 1.0 and 28.0, respectively. Among the 13 machine learning algorithms, the XGBoost model demonstrated superior performance. The final multivariate screening model achieved an area under the curve (AUC) of 0.887, a sensitivity of 0.787, a specificity of 0.830, and an accuracy of 0.816 in classifying young adults' concurrent depression risk. The SHAP analysis showed the importance of each variable: anxiety (2.303) > resilience (0.774) > parent-child relationship (0.708) > mobile phone usage time (0.411). The final multivariate model exhibited stable performance during cross-validation (AUC = 0.885 ± 0.032), significantly better than the single-variable model (P < 0.001) and better screening reliability (Brier score 0.153). The final multivariate XGBoost model provides a highly accurate and interpretable approach for young adults' depression risk stratification. As the model was developed using cross-sectional data collected during the COVID-19 campus lockdown, prospective validation is required before clinical deployment. Notably, anxiety level emerged as the most influential risk factor, and resilience demonstrated a significant protective effect.
The rapid expansion of screen exposure represents a growing public health concern, yet existing measures often overlook complex usage patterns such as multitasking. This study developed and validated a multidimensional Screen Time Assessment (STA) tool among undergraduate students. The questionnaire development involved literature review, expert consultation, content validation, reliability analysis, item analysis, and exploratory factor analysis (EFA). The final tool comprised 35 items across four domains, representing high content validity (I-CVI range: 0.97–1.00), satisfactory internal consistency (Cronbach’s α = 0.830; Spearman–Brown = 0.828; Guttman = 0.815), significant item–total correlations (
p
< .001), and EFA supported the structure (KMO = 0.797; Bartlett’s test
p
< .001. STA is a valid and reliable instrument that captures duration, activities performed, and multitasking behavior. The tool can be used as a valid psychoeducational measure to identify problematic screen use, design targeted interventions, and decision-making in public health, education, and digital governance.
Palka Mittal, Gaurav Aggarwal, Sheetal Yadav et al.· Journal of Psychoeducational...· 0 citations
Background: Medical degree programs are internationally recognized as among the most academically rigorous forms of professional training. In this demanding environment, exam-related anxiety constitutes a substantial psychological burden, yet its magnitude among medical undergraduates in Libya has received limited empirical attention. This investigation sought to quantify examination anxiety levels among students enrolled at the Faculty of Medicine, University of Zawia, and to evaluate associations between anxiety severity and selected demographic and academic characteristics. Methods: This cross-sectional survey-based study was employed, utilising a 38-item, psychometrically validated questionnaire administered to students spanning all academic stages (first year through internship). Item responses were captured on a four-point Likert scale (0 = strongly disagree to 3 = strongly agree). Anxiety severity was categorised as mild, moderate, severe, or very severe based on mean per-item scores. Inferential analyses comprised the Mann-Whitney U test, the Kruskal-Wallis H test, the chi-square test, and Pearson correlation coefficients. Result: Two hundred and thirty-four students participated (female: 83.3%; male: 16.7%; mean age: 22.2 ± 2.3 years). The instrument exhibited excellent internal consistency (Cronbach’s α = 0.931). The cohort mean total anxiety score was 63.7 ± 22.1 out of a maximum of 114 (55.9% of the attainable maximum), corresponding to a mean per-item score of 1.68 ± 0.58. The majority of participants (58.1%) met criteria for severe or very severe anxiety. Female students returned significantly elevated scores relative to their male counterparts (65.1 ± 21.7 versus 56.5 ± 23.0; p = 0.023). No statistically significant variation was detected across academic year (p = 0.147), academic standing (p = 0.354), or age (r = −0.066, p = 0.319). Items attracting the highest endorsement rates included sleep impairment arising from pre-examination rumination (90.2%), pervasive tension during final examination preparation (87.6%), and the desire to be relieved of examination-related distress (89.7%). Conclusion: This study has found that examination anxiety is highly prevalent within this cohort, with the majority of students reporting severe or very severe symptomatology. Female students are disproportionately affected. These findings underscore an urgent requirement for systematically delivered psychological support, tailored stress-management programs, and structural reforms within the curriculum.
Omar Alrawi, Ayoub Alfarah, Ghaida Alshebani et al.· Libyan journal of medical re...· 0 citations
Growing concerns about the psychological impact of generative AI have highlighted the need for better measurement tools. Existing scales are limited by narrow samples and a failure to distinguish anxiety from stress. This research introduces the Artificial Intelligence Stress and Anxiety Scale (AISAS), a psychometrically validated instrument assessing both constructs in the general population. Development followed best-practice guidelines across two preregistered studies using representative UK adult samples for age, gender, and ethnicity. An initial 62-item pool was refined via expert ratings and a Content Validity Index. Exploratory factor analysis (Study 1,
N
= 301) identified four factors: job-related concerns, AI adaptation stress, privacy concerns, and existential/consciousness concerns, explaining 72% of variance. The adaptation stress dimension was retained as a brief two-item indicator (
r
= .68, Spearman-Brown ρ = 0.81) rather than a fully developed subscale. The general scale showed excellent internal consistency (α = 0.92), full gender measurement invariance, and preliminary evidence of convergent, discriminant, and criterion-related validity. Specific dimensions differentially predicted AI usage frequency and behavioral intention. Study 2 (
N
= 324) confirmed the factor structure’s stability across an independent sample. Descriptive findings suggest privacy concerns are common, while stress related to adapting to and learning about AI is relatively infrequent. Overall, the AISAS provides a reliable, multidimensional tool for researchers and practitioners assessing psychological responses to artificial intelligence.
Enrico Cipriani, Hanna Joy Justesen, D. Menicucci et al.· Scientific Reports· 0 citations
Depression is a leading cause of disability among university students. While medical students are often assumed to be at highest risk, cross-disciplinary comparisons in South Asia remain scarce. This study examined depressive symptom differences between medical and non-medical students in Pakistan and evaluated the concurrent psychometric performance of the PHQ-9 and DASS-21.
A cross-sectional survey of 602 undergraduate students (424 medical, 178 non-medical) was conducted at universities in Lahore, Pakistan. Measures included the PHQ-9 and DASS-21. Analyses comprised descriptive statistics, Wilcoxon rank-sum tests, multivariable linear regression (separate models for PHQ-9 and each DASS-21 domain), confirmatory factor analysis, item response theory using a graded response model, and symptom network analysis with stability testing.
Non-medical students had higher PHQ-9 scores than medical students (median 10.0 vs. 9.0). In adjusted models, non-medical students scored higher on PHQ-9 (β = −1.43,
p
= 0.004), DASS Depression (β = −1.82,
p
= 0.003), and DASS Anxiety (β = −2.31,
p
= 0.001); the DASS Stress difference was non-significant (β = −0.89,
p
= 0.089). Female gender predicted higher scores across all domains (all
p
< 0.001). Each additional hour of sleep was associated with lower PHQ-9 scores (β = −0.37,
p
= 0.010). Prior depression treatment was the strongest predictor (β = 4.15,
p
< 0.001). DASS-21 confirmatory factor analysis showed adequate fit (CFI = 0.954, TLI = 0.948, RMSEA = 0.052), but latent factor correlations were very high (Depression–Stress
r
= 0.939; Anxiety–Stress
r
= 0.949). Cronbach’s α ranged 0.82–0.88 across scales. Item response theory indicated that self-worth, concentration, down, and appetite items showed the highest discrimination; interest (anhedonia) showed the lowest (
a
= 0.468). In network analysis, self-worth, concentration, and downheartedness were the most central nodes; stability coefficients ranged 0.31–0.44. Measurement invariance across discipline was supported. Model R² values were low (0.029–0.041).
Non-medical students reported higher depressive and anxiety symptoms than medical students in this Pakistani sample, although academic discipline explained only a small proportion of variance. Both instruments performed adequately, though the DASS-21’s near-unity latent correlations may raise questions about subscale distinguishability in this population. Cognitive and self-worth symptoms emerged as the most informative for screening purposes. Campus mental health strategies should address the whole student population, with particular attention to female students and sleep health.
Taimoor Asghar, A. Hassan, Ifrah Sahar et al.· BMC Psychology· 0 citations
